2 research outputs found
OCR Graph Features for Manipulation Detection in Documents
Detecting manipulations in digital documents is becoming increasingly
important for information verification purposes. Due to the proliferation of
image editing software, altering key information in documents has become widely
accessible. Nearly all approaches in this domain rely on a procedural approach,
using carefully generated features and a hand-tuned scoring system, rather than
a data-driven and generalizable approach. We frame this issue as a graph
comparison problem using the character bounding boxes, and propose a model that
leverages graph features using OCR (Optical Character Recognition). Our model
relies on a data-driven approach to detect alterations by training a random
forest classifier on the graph-based OCR features. We evaluate our algorithm's
forgery detection performance on dataset constructed from real business
documents with slight forgery imperfections. Our proposed model dramatically
outperforms the most closely-related document manipulation detection model on
this task
Security and Privacy for the Internet of Things: An Overview of the Project
As the adoption of digital technologies expands, it becomes vital to build trust and confidence in the integrity of such technology. The SPIRIT project investigates the proof of concept of employing novel secure and privacy-ensuring techniques in services set-up in the Internet of Things (IoT) environment, aiming to increase the trust of users in IoTbased systems. The proposed system integrates three highly novel technology concepts developed by the consortium partners. Specifically, a technology, termed ICMetrics, for deriving encryption keys directly from the operating characteristics of digital devices; secondly, a technology based on a contentbased signature of user data in order to ensure the integrity of sent data upon arrival; a third technology, termed semantic firewall, which is able to allow or deny the transmission of data derived from an IoT device according to the information contained within the data and the information gathered about the requester